Managing aquaculture ponds is vital for environmental monitoring and conservation. This tutorial presents how to leverage satellite imagery and semantic segmentation models to detect and map aquaculture ponds based on production intensity.
Author(s):
- Joshua Cortez, Thinking Machines Data Science
- JC Nacpil, Thinking Machines Data Science
- Email: jcnacpil@thinkingmachin.es
Originally presented at NeurIPS 2023
Link to Tutorial Intro presentation
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
Estimated time to execute end-to-end: 45 minutes
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Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Cortez, J., & Nacpil, J. C. (2023). Aquaculture Mapping: Detecting and Classifying Aquaculture Ponds using Deep Learning [Tutorial]. In Conference on Neural Information Processing Systems. Climate Change AI. https://doi.org/10.5281/zenodo.11584995
@misc{cortez2023aquaculture,
title={Aquaculture Mapping: Detecting and Classifying Aquaculture Ponds using Deep Learning},
author={Cortez, Joshua and Nacpil, John Christian},
year={2023},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11584995},
booktitle={Conference on Neural Information Processing Systems},
howpublished={\url{https://github.com/climatechange-ai-tutorials/aquaculture-mapping}}
}